A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition
Bibliographic record
Abstract
Based on class center and discriminant analysis, this paper puts forward a novel bi-objective optimization method that preserves the neighborhood structure, and applies it to facial recognition. Firstly, the locally preserving projection (LPP) was improved into the class-center locally preserving projection (CLPP) by replacing the sample-based neighborhood structure with the class center-based neighborhood structure. Next, a bi-objective optimization model was developed based on the CLPP and linear discriminant analysis (LDA), and solved by the multi-objective optimization theory. The bi-objective optimization problem combines the merits of single-target CLPP and single-target LDA: the class center-based neighborhood structure is preserved, and the class information is introduced naturally, making up for the defect of the LDA due to the manual changes of adjacency coefficient and highlighting the physical meaning. Finally, several experiments were conducted on AR, CAS-60 and FERET face databases. The experimental results prove that our methods are correct and effective, and the bi-objective optimization method based on CLPP and LDA (CLPP+LDA) achieved the best recognition effect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".